Overview

Dataset statistics

Number of variables25
Number of observations1000000
Missing cells7526151
Missing cells (%)30.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory190.7 MiB
Average record size in memory200.0 B

Variable types

Categorical17
Numeric8

Alerts

ad_type has constant value "Propiedad" Constant
price_period has constant value "Mensual" Constant
id has a high cardinality: 1000000 distinct values High cardinality
start_date has a high cardinality: 371 distinct values High cardinality
end_date has a high cardinality: 449 distinct values High cardinality
created_on has a high cardinality: 371 distinct values High cardinality
l3 has a high cardinality: 343 distinct values High cardinality
l4 has a high cardinality: 64 distinct values High cardinality
l6 has a high cardinality: 164 distinct values High cardinality
title has a high cardinality: 364029 distinct values High cardinality
description has a high cardinality: 512446 distinct values High cardinality
rooms is highly correlated with bedrooms and 3 other fieldsHigh correlation
bedrooms is highly correlated with rooms and 3 other fieldsHigh correlation
bathrooms is highly correlated with rooms and 4 other fieldsHigh correlation
surface_total is highly correlated with rooms and 4 other fieldsHigh correlation
surface_covered is highly correlated with rooms and 4 other fieldsHigh correlation
price is highly correlated with bathrooms and 2 other fieldsHigh correlation
rooms is highly correlated with bedrooms and 1 other fieldsHigh correlation
bedrooms is highly correlated with rooms and 1 other fieldsHigh correlation
bathrooms is highly correlated with rooms and 1 other fieldsHigh correlation
rooms is highly correlated with bedrooms and 3 other fieldsHigh correlation
bedrooms is highly correlated with rooms and 3 other fieldsHigh correlation
bathrooms is highly correlated with rooms and 3 other fieldsHigh correlation
surface_total is highly correlated with rooms and 3 other fieldsHigh correlation
surface_covered is highly correlated with rooms and 3 other fieldsHigh correlation
property_type is highly correlated with ad_type and 1 other fieldsHigh correlation
l2 is highly correlated with ad_type and 4 other fieldsHigh correlation
ad_type is highly correlated with property_type and 7 other fieldsHigh correlation
currency is highly correlated with ad_type and 1 other fieldsHigh correlation
l5 is highly correlated with l2 and 4 other fieldsHigh correlation
l4 is highly correlated with l2 and 4 other fieldsHigh correlation
operation_type is highly correlated with ad_type and 1 other fieldsHigh correlation
l1 is highly correlated with l2 and 4 other fieldsHigh correlation
price_period is highly correlated with property_type and 7 other fieldsHigh correlation
lat is highly correlated with lon and 3 other fieldsHigh correlation
lon is highly correlated with latHigh correlation
l1 is highly correlated with l2High correlation
l2 is highly correlated with lat and 2 other fieldsHigh correlation
l4 is highly correlated with lat and 2 other fieldsHigh correlation
l5 is highly correlated with lat and 1 other fieldsHigh correlation
rooms is highly correlated with bedrooms and 1 other fieldsHigh correlation
bedrooms is highly correlated with roomsHigh correlation
bathrooms is highly correlated with roomsHigh correlation
lat has 259719 (26.0%) missing values Missing
lon has 259718 (26.0%) missing values Missing
l3 has 62812 (6.3%) missing values Missing
l4 has 726005 (72.6%) missing values Missing
l5 has 840873 (84.1%) missing values Missing
l6 has 945253 (94.5%) missing values Missing
rooms has 827913 (82.8%) missing values Missing
bedrooms has 623954 (62.4%) missing values Missing
bathrooms has 200507 (20.1%) missing values Missing
surface_total has 958758 (95.9%) missing values Missing
surface_covered has 940091 (94.0%) missing values Missing
price_period has 878833 (87.9%) missing values Missing
surface_covered is highly skewed (γ1 = 176.2420211) Skewed
price is highly skewed (γ1 = 315.3462535) Skewed
id is uniformly distributed Uniform
id has unique values Unique
bedrooms has 31356 (3.1%) zeros Zeros

Reproduction

Analysis started2021-10-20 23:42:59.113938
Analysis finished2021-10-20 23:53:06.010674
Duration10 minutes and 6.9 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct1000000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
KsjahK62rxcYKXXQjOdkqw==
 
1
OltPpW3A+j+Gm03MT5HWxw==
 
1
2JCCy/+Bw9AMAwKYgwCSVA==
 
1
hFw4jUlcDzMl2Ks0fAkXRg==
 
1
Zg0vkOO0x66JMP5SOo+ZqA==
 
1
Other values (999995)
999995 

Length

Max length24
Median length24
Mean length24
Min length24

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000000 ?
Unique (%)100.0%

Sample

1st rowKsjahK62rxcYKXXQjOdkqw==
2nd rowY+gsBZYq1zu5NoR3V5oUGA==
3rd rowJpzqxj8/Vgf3Aa5ASxUBNg==
4th rowieuFnkFx/yHDD66iMV14Gw==
5th rowg4u5JM+hAHEk8SukRSjMzg==

Common Values

ValueCountFrequency (%)
KsjahK62rxcYKXXQjOdkqw==1
 
< 0.1%
OltPpW3A+j+Gm03MT5HWxw==1
 
< 0.1%
2JCCy/+Bw9AMAwKYgwCSVA==1
 
< 0.1%
hFw4jUlcDzMl2Ks0fAkXRg==1
 
< 0.1%
Zg0vkOO0x66JMP5SOo+ZqA==1
 
< 0.1%
cSbm8NkM1bcfQlhgBH2iSg==1
 
< 0.1%
4Ds3xWhgtHn4hxDLfQTd7w==1
 
< 0.1%
S10GHxOqheGG4KrCT3/pjw==1
 
< 0.1%
Ey8dCpEV9nj7KxZazhA0Dg==1
 
< 0.1%
f30ZbE+miZYeNZkT00ecFw==1
 
< 0.1%
Other values (999990)999990
> 99.9%

Length

2021-10-20T18:53:06.189640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ksjahk62rxcykxxqjodkqw1
 
< 0.1%
lyb0igia93isqljtbdsv3w1
 
< 0.1%
tcr0yitx/s6ebjwwueptbw1
 
< 0.1%
0/yuotnxoy9ezx/h9vbqga1
 
< 0.1%
jpzqxj8/vgf3aa5asxubng1
 
< 0.1%
ieufnkfx/yhdd66imv14gw1
 
< 0.1%
g4u5jm+hahek8sukrsjmzg1
 
< 0.1%
s9t8nkj/yyndmxxrz1emq1
 
< 0.1%
0dbic9qjv2fl9oq0s+xasa1
 
< 0.1%
0/s4pbwarkyd3ndmyzztpq1
 
< 0.1%
Other values (999990)999990
> 99.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ad_type
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
Propiedad
1000000 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPropiedad
2nd rowPropiedad
3rd rowPropiedad
4th rowPropiedad
5th rowPropiedad

Common Values

ValueCountFrequency (%)
Propiedad1000000
100.0%

Length

2021-10-20T18:53:06.294642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-20T18:53:06.361675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
propiedad1000000
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

start_date
Categorical

HIGH CARDINALITY

Distinct371
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
2020-08-27
 
13627
2020-07-27
 
10260
2020-09-25
 
9807
2021-02-15
 
9578
2021-02-16
 
9186
Other values (366)
947542 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-10-07
2nd row2020-10-07
3rd row2020-10-07
4th row2020-10-07
5th row2020-10-07

Common Values

ValueCountFrequency (%)
2020-08-2713627
 
1.4%
2020-07-2710260
 
1.0%
2020-09-259807
 
1.0%
2021-02-159578
 
1.0%
2021-02-169186
 
0.9%
2020-08-208300
 
0.8%
2020-12-176755
 
0.7%
2020-10-236725
 
0.7%
2020-10-076716
 
0.7%
2020-11-236703
 
0.7%
Other values (361)912343
91.2%

Length

2021-10-20T18:53:06.436677image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-08-2713627
 
1.4%
2020-07-2710260
 
1.0%
2020-09-259807
 
1.0%
2021-02-159578
 
1.0%
2021-02-169186
 
0.9%
2020-08-208300
 
0.8%
2020-12-176755
 
0.7%
2020-10-236725
 
0.7%
2020-10-076716
 
0.7%
2020-11-236703
 
0.7%
Other values (361)912343
91.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

end_date
Categorical

HIGH CARDINALITY

Distinct449
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
9999-12-31
143986 
2020-08-27
 
9544
2020-11-13
 
9483
2021-02-15
 
8945
2021-02-16
 
7560
Other values (444)
820482 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-10-09
2nd row2021-01-06
3rd row2020-10-07
4th row2021-04-12
5th row9999-12-31

Common Values

ValueCountFrequency (%)
9999-12-31143986
 
14.4%
2020-08-279544
 
1.0%
2020-11-139483
 
0.9%
2021-02-158945
 
0.9%
2021-02-167560
 
0.8%
2021-03-026848
 
0.7%
2021-03-266546
 
0.7%
2021-03-256526
 
0.7%
2020-09-096278
 
0.6%
2020-12-215979
 
0.6%
Other values (439)788305
78.8%

Length

2021-10-20T18:53:06.540642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9999-12-31143986
 
14.4%
2020-08-279544
 
1.0%
2020-11-139483
 
0.9%
2021-02-158945
 
0.9%
2021-02-167560
 
0.8%
2021-03-026848
 
0.7%
2021-03-266546
 
0.7%
2021-03-256526
 
0.7%
2020-09-096278
 
0.6%
2020-12-215979
 
0.6%
Other values (439)788305
78.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

created_on
Categorical

HIGH CARDINALITY

Distinct371
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
2020-08-27
 
13627
2020-07-27
 
10260
2020-09-25
 
9807
2021-02-15
 
9578
2021-02-16
 
9186
Other values (366)
947542 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-10-07
2nd row2020-10-07
3rd row2020-10-07
4th row2020-10-07
5th row2020-10-07

Common Values

ValueCountFrequency (%)
2020-08-2713627
 
1.4%
2020-07-2710260
 
1.0%
2020-09-259807
 
1.0%
2021-02-159578
 
1.0%
2021-02-169186
 
0.9%
2020-08-208300
 
0.8%
2020-12-176755
 
0.7%
2020-10-236725
 
0.7%
2020-10-076716
 
0.7%
2020-11-236703
 
0.7%
Other values (361)912343
91.2%

Length

2021-10-20T18:53:06.657678image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-08-2713627
 
1.4%
2020-07-2710260
 
1.0%
2020-09-259807
 
1.0%
2021-02-159578
 
1.0%
2021-02-169186
 
0.9%
2020-08-208300
 
0.8%
2020-12-176755
 
0.7%
2020-10-236725
 
0.7%
2020-10-076716
 
0.7%
2020-11-236703
 
0.7%
Other values (361)912343
91.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

lat
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct168888
Distinct (%)22.8%
Missing259719
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean5.822492722
Minimum-75.64033
Maximum85.05112878
Zeros0
Zeros (%)0.0%
Negative35
Negative (%)< 0.1%
Memory size7.6 MiB
2021-10-20T18:53:06.779639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-75.64033
5-th percentile3.3715569
Q14.642
median5.0470197
Q36.3365977
95-th percentile11.003
Maximum85.05112878
Range160.6914588
Interquartile range (IQR)1.6945977

Descriptive statistics

Standard deviation2.239671814
Coefficient of variation (CV)0.3846585854
Kurtosis47.59916879
Mean5.822492722
Median Absolute Deviation (MAD)1.1570197
Skewness0.04041099925
Sum4310280.734
Variance5.016129833
MonotonicityNot monotonic
2021-10-20T18:53:07.097675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.1191960
 
0.2%
11.0061814
 
0.2%
6.2031272
 
0.1%
11.0011218
 
0.1%
11.011147
 
0.1%
11.0071146
 
0.1%
6.16728651118
 
0.1%
111117
 
0.1%
11.0041048
 
0.1%
11.0151016
 
0.1%
Other values (168878)727425
72.7%
(Missing)259719
 
26.0%
ValueCountFrequency (%)
-75.640331
< 0.1%
-75.601571
< 0.1%
-75.594291
< 0.1%
-75.580771
< 0.1%
-75.575891
< 0.1%
-75.575241
< 0.1%
-75.573941
< 0.1%
-75.573141
< 0.1%
-75.571722
< 0.1%
-75.568681
< 0.1%
ValueCountFrequency (%)
85.051128781
< 0.1%
51.8012311
< 0.1%
51.77040721
< 0.1%
51.75698771
< 0.1%
44.41159681
< 0.1%
42.57505541
< 0.1%
42.34813622
< 0.1%
41.698169551
< 0.1%
40.02499642
< 0.1%
39.94352121
< 0.1%

lon
Real number (ℝ)

HIGH CORRELATION
MISSING

Distinct164767
Distinct (%)22.3%
Missing259718
Missing (%)26.0%
Infinite0
Infinite (%)0.0%
Mean-74.89478525
Minimum-180
Maximum100.4774978
Zeros0
Zeros (%)0.0%
Negative740247
Negative (%)74.0%
Memory size7.6 MiB
2021-10-20T18:53:07.282674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-180
5-th percentile-76.541
Q1-75.60342625
median-75.10748884
Q3-74.05588887
95-th percentile-73.085
Maximum100.4774978
Range280.4774978
Interquartile range (IQR)1.547537375

Descriptive statistics

Standard deviation1.309072276
Coefficient of variation (CV)-0.01747881741
Kurtosis1398.795296
Mean-74.89478525
Median Absolute Deviation (MAD)0.9984106871
Skewness15.39104817
Sum-55443261.42
Variance1.713670224
MonotonicityNot monotonic
2021-10-20T18:53:07.423673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-74.0492224
 
0.2%
-74.0521925
 
0.2%
-74.0531918
 
0.2%
-74.051849
 
0.2%
-74.0481843
 
0.2%
-74.0511687
 
0.2%
-74.0541654
 
0.2%
-74.0471626
 
0.2%
-76.5291613
 
0.2%
-74.0551588
 
0.2%
Other values (164757)722355
72.2%
(Missing)259718
 
26.0%
ValueCountFrequency (%)
-1801
< 0.1%
-121.8991421
< 0.1%
-121.32631871
< 0.1%
-119.698191
< 0.1%
-111.0017041
< 0.1%
-101.19498251
< 0.1%
-98.264634731
< 0.1%
-97.49415161
< 0.1%
-94.16051451
< 0.1%
-93.60162041
< 0.1%
ValueCountFrequency (%)
100.47749781
< 0.1%
75.596511
< 0.1%
14.26615571
< 0.1%
14.26097611
< 0.1%
14.15448831
< 0.1%
13.36148681
< 0.1%
6.324011
< 0.1%
6.250971
< 0.1%
6.249781
< 0.1%
6.240561
< 0.1%

l1
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
Colombia
999998 
Estados Unidos
 
2

Length

Max length14
Median length8
Mean length8.000012
Min length8

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowColombia
2nd rowColombia
3rd rowColombia
4th rowColombia
5th rowColombia

Common Values

ValueCountFrequency (%)
Colombia999998
> 99.9%
Estados Unidos2
 
< 0.1%

Length

2021-10-20T18:53:07.557676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-20T18:53:07.638639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
colombia999998
> 99.9%
estados2
 
< 0.1%
unidos2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

l2
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
Antioquia
341453 
Cundinamarca
208918 
Valle del Cauca
117770 
Atlántico
78605 
Santander
71737 
Other values (27)
181517 

Length

Max length39
Median length9
Mean length10.282235
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowValle del Cauca
2nd rowValle del Cauca
3rd rowValle del Cauca
4th rowValle del Cauca
5th rowValle del Cauca

Common Values

ValueCountFrequency (%)
Antioquia341453
34.1%
Cundinamarca208918
20.9%
Valle del Cauca117770
 
11.8%
Atlántico78605
 
7.9%
Santander71737
 
7.2%
Caldas56296
 
5.6%
Norte de Santander32247
 
3.2%
Risaralda28505
 
2.9%
Quindío11753
 
1.2%
Bolívar11016
 
1.1%
Other values (22)41700
 
4.2%

Length

2021-10-20T18:53:07.723639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
antioquia341453
26.3%
cundinamarca208918
16.1%
cauca126256
 
9.7%
valle117770
 
9.1%
del117770
 
9.1%
santander103984
 
8.0%
atlántico78605
 
6.0%
caldas56296
 
4.3%
norte32247
 
2.5%
de32247
 
2.5%
Other values (30)85067
 
6.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

l3
Categorical

HIGH CARDINALITY
MISSING

Distinct343
Distinct (%)< 0.1%
Missing62812
Missing (%)6.3%
Memory size7.6 MiB
Medellín
262856 
Bogotá D.C
171168 
Cali
95401 
Barranquilla
74801 
Bucaramanga
46595 
Other values (338)
286367 

Length

Max length26
Median length8
Mean length8.406024192
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)< 0.1%

Sample

1st rowCali
2nd rowCali
3rd rowCali
4th rowCali
5th rowCali

Common Values

ValueCountFrequency (%)
Medellín262856
26.3%
Bogotá D.C171168
17.1%
Cali95401
 
9.5%
Barranquilla74801
 
7.5%
Bucaramanga46595
 
4.7%
Manizales44505
 
4.5%
Envigado24171
 
2.4%
Cúcuta21510
 
2.2%
Pereira18855
 
1.9%
Floridablanca13830
 
1.4%
Other values (333)163496
16.3%
(Missing)62812
 
6.3%

Length

2021-10-20T18:53:07.850643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
medellín262856
23.3%
d.c171168
15.2%
bogotá171168
15.2%
cali95401
 
8.5%
barranquilla74801
 
6.6%
bucaramanga46595
 
4.1%
manizales44505
 
4.0%
envigado24171
 
2.1%
cúcuta21510
 
1.9%
pereira18855
 
1.7%
Other values (358)195505
17.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

l4
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct64
Distinct (%)< 0.1%
Missing726005
Missing (%)72.6%
Memory size7.6 MiB
Zona Chapinero
49905 
Zona Norte
43352 
Zona Noroccidental
28896 
El Poblado
27038 
Zona Occidental
17226 
Other values (59)
107578 

Length

Max length22
Median length10
Mean length11.92244019
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCiudad Jardín
2nd rowCiudad Jardín
3rd rowCiudad Jardín
4th rowSan Fernando Nuevo
5th rowCiudad Jardín

Common Values

ValueCountFrequency (%)
Zona Chapinero49905
 
5.0%
Zona Norte43352
 
4.3%
Zona Noroccidental28896
 
2.9%
El Poblado27038
 
2.7%
Zona Occidental17226
 
1.7%
Zona Suroccidental12980
 
1.3%
Laureles12815
 
1.3%
Zona Centro10862
 
1.1%
Belén9755
 
1.0%
Lili7118
 
0.7%
Other values (54)54048
 
5.4%
(Missing)726005
72.6%

Length

2021-10-20T18:53:07.978675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
zona169805
33.7%
chapinero49905
 
9.9%
norte43352
 
8.6%
el32031
 
6.3%
noroccidental28896
 
5.7%
poblado27038
 
5.4%
occidental17226
 
3.4%
suroccidental12980
 
2.6%
laureles12815
 
2.5%
centro10862
 
2.2%
Other values (73)99552
19.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

l5
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct20
Distinct (%)< 0.1%
Missing840873
Missing (%)84.1%
Memory size7.6 MiB
Usaquén
43294 
Chapinero
36034 
Suba
18284 
Kennedy
9082 
Engativa
8635 
Other values (15)
43798 

Length

Max length18
Median length8
Mean length8.123216048
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSuba
2nd rowEngativa
3rd rowTeusaquillo
4th rowEngativa
5th rowUsaquén

Common Values

ValueCountFrequency (%)
Usaquén43294
 
4.3%
Chapinero36034
 
3.6%
Suba18284
 
1.8%
Kennedy9082
 
0.9%
Engativa8635
 
0.9%
Fontibón8588
 
0.9%
Teusaquillo7327
 
0.7%
Barrios Unidos6544
 
0.7%
Santa Fe4721
 
0.5%
Bosa3897
 
0.4%
Other values (10)12721
 
1.3%
(Missing)840873
84.1%

Length

2021-10-20T18:53:08.114640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
usaquén43294
23.7%
chapinero36034
19.7%
suba18284
10.0%
kennedy9082
 
5.0%
engativa8635
 
4.7%
fontibón8588
 
4.7%
teusaquillo7327
 
4.0%
barrios6544
 
3.6%
unidos6544
 
3.6%
santa4721
 
2.6%
Other values (19)33924
18.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

l6
Categorical

HIGH CARDINALITY
MISSING

Distinct164
Distinct (%)0.3%
Missing945253
Missing (%)94.5%
Memory size7.6 MiB
Chico Reservado
10687 
Santa Barbara
7095 
Cedritos
6862 
Los Rosales
2744 
Chico Norte
 
1983
Other values (159)
25376 

Length

Max length22
Median length11
Mean length11.60677297
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCofradía
2nd rowLa Merced
3rd rowModelia
4th rowEl Batán
5th rowSanta Barbara

Common Values

ValueCountFrequency (%)
Chico Reservado10687
 
1.1%
Santa Barbara7095
 
0.7%
Cedritos6862
 
0.7%
Los Rosales2744
 
0.3%
Chico Norte1983
 
0.2%
Bella Suiza1808
 
0.2%
Ciudad Salitre1762
 
0.2%
Modelia1522
 
0.2%
El Batán1280
 
0.1%
El Chicó1177
 
0.1%
Other values (154)17827
 
1.8%
(Missing)945253
94.5%

Length

2021-10-20T18:53:08.249673image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chico13347
 
13.8%
reservado10687
 
11.0%
santa8268
 
8.5%
barbara7095
 
7.3%
cedritos6862
 
7.1%
el3641
 
3.8%
los2788
 
2.9%
rosales2744
 
2.8%
norte2503
 
2.6%
salitre1940
 
2.0%
Other values (188)36895
38.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

rooms
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct37
Distinct (%)< 0.1%
Missing827913
Missing (%)82.8%
Infinite0
Infinite (%)0.0%
Mean3.124123263
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2021-10-20T18:53:08.380640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile6
Maximum40
Range39
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.802065106
Coefficient of variation (CV)0.576822665
Kurtosis47.78869461
Mean3.124123263
Median Absolute Deviation (MAD)1
Skewness4.908060291
Sum537621
Variance3.247438648
MonotonicityNot monotonic
2021-10-20T18:53:08.526639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
384435
 
8.4%
232761
 
3.3%
422022
 
2.2%
116435
 
1.6%
57363
 
0.7%
63355
 
0.3%
71623
 
0.2%
81192
 
0.1%
9670
 
0.1%
10659
 
0.1%
Other values (27)1572
 
0.2%
(Missing)827913
82.8%
ValueCountFrequency (%)
116435
 
1.6%
232761
 
3.3%
384435
8.4%
422022
 
2.2%
57363
 
0.7%
63355
 
0.3%
71623
 
0.2%
81192
 
0.1%
9670
 
0.1%
10659
 
0.1%
ValueCountFrequency (%)
407
 
< 0.1%
361
 
< 0.1%
351
 
< 0.1%
342
 
< 0.1%
331
 
< 0.1%
322
 
< 0.1%
315
 
< 0.1%
306
 
< 0.1%
2921
< 0.1%
2813
< 0.1%

bedrooms
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct59
Distinct (%)< 0.1%
Missing623954
Missing (%)62.4%
Infinite0
Infinite (%)0.0%
Mean2.807143275
Minimum0
Maximum96
Zeros31356
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2021-10-20T18:53:08.679639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q33
95-th percentile5
Maximum96
Range96
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.952432275
Coefficient of variation (CV)0.6955228442
Kurtosis132.7735379
Mean2.807143275
Median Absolute Deviation (MAD)1
Skewness6.474984068
Sum1055615
Variance3.811991788
MonotonicityNot monotonic
2021-10-20T18:53:08.830675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3171639
 
17.2%
267335
 
6.7%
440991
 
4.1%
134844
 
3.5%
031356
 
3.1%
513672
 
1.4%
66124
 
0.6%
72898
 
0.3%
82163
 
0.2%
101430
 
0.1%
Other values (49)3594
 
0.4%
(Missing)623954
62.4%
ValueCountFrequency (%)
031356
 
3.1%
134844
 
3.5%
267335
 
6.7%
3171639
17.2%
440991
 
4.1%
513672
 
1.4%
66124
 
0.6%
72898
 
0.3%
82163
 
0.2%
91145
 
0.1%
ValueCountFrequency (%)
961
 
< 0.1%
821
 
< 0.1%
771
 
< 0.1%
702
 
< 0.1%
681
 
< 0.1%
663
 
< 0.1%
651
 
< 0.1%
611
 
< 0.1%
6012
< 0.1%
582
 
< 0.1%

bathrooms
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct20
Distinct (%)< 0.1%
Missing200507
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean2.414535212
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2021-10-20T18:53:08.959640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile5
Maximum20
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.412620473
Coefficient of variation (CV)0.5850486117
Kurtosis12.63374304
Mean2.414535212
Median Absolute Deviation (MAD)1
Skewness2.470868211
Sum1930404
Variance1.995496602
MonotonicityNot monotonic
2021-10-20T18:53:09.076674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2359030
35.9%
1178448
17.8%
3132983
 
13.3%
471128
 
7.1%
532495
 
3.2%
612414
 
1.2%
74457
 
0.4%
103432
 
0.3%
82651
 
0.3%
91610
 
0.2%
Other values (10)845
 
0.1%
(Missing)200507
20.1%
ValueCountFrequency (%)
1178448
17.8%
2359030
35.9%
3132983
 
13.3%
471128
 
7.1%
532495
 
3.2%
612414
 
1.2%
74457
 
0.4%
82651
 
0.3%
91610
 
0.2%
103432
 
0.3%
ValueCountFrequency (%)
20122
< 0.1%
1921
 
< 0.1%
1847
 
< 0.1%
1738
 
< 0.1%
1648
 
< 0.1%
1572
< 0.1%
1484
< 0.1%
1392
< 0.1%
12177
< 0.1%
11144
< 0.1%

surface_total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2147
Distinct (%)5.2%
Missing958758
Missing (%)95.9%
Infinite0
Infinite (%)0.0%
Mean982.6312254
Minimum10
Maximum200000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2021-10-20T18:53:09.234672image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile35
Q164
median100
Q3223.75
95-th percentile2500
Maximum200000
Range199990
Interquartile range (IQR)159.75

Descriptive statistics

Standard deviation7013.950521
Coefficient of variation (CV)7.137927576
Kurtosis362.4855805
Mean982.6312254
Median Absolute Deviation (MAD)47
Skewness17.18950437
Sum40525677
Variance49195501.91
MonotonicityNot monotonic
2021-10-20T18:53:09.395639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
601327
 
0.1%
70895
 
0.1%
80819
 
0.1%
90761
 
0.1%
65739
 
0.1%
50625
 
0.1%
100590
 
0.1%
120587
 
0.1%
75541
 
0.1%
55523
 
0.1%
Other values (2137)33835
 
3.4%
(Missing)958758
95.9%
ValueCountFrequency (%)
1079
< 0.1%
1126
 
< 0.1%
1261
< 0.1%
1334
< 0.1%
1441
< 0.1%
1568
< 0.1%
1648
< 0.1%
1750
< 0.1%
1865
< 0.1%
1934
< 0.1%
ValueCountFrequency (%)
2000001
< 0.1%
1995791
< 0.1%
1994821
< 0.1%
1984001
< 0.1%
1920002
< 0.1%
1900001
< 0.1%
1834611
< 0.1%
1823391
< 0.1%
1800002
< 0.1%
1794691
< 0.1%

surface_covered
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
SKEWED

Distinct1513
Distinct (%)2.5%
Missing940091
Missing (%)94.0%
Infinite0
Infinite (%)0.0%
Mean1502.722663
Minimum1
Maximum31520100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2021-10-20T18:53:09.652639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile34
Q162
median96
Q3188
95-th percentile580
Maximum31520100
Range31520099
Interquartile range (IQR)126

Descriptive statistics

Standard deviation152096.469
Coefficient of variation (CV)101.2139317
Kurtosis33747.8832
Mean1502.722663
Median Absolute Deviation (MAD)43
Skewness176.2420211
Sum90026612
Variance2.313333588 × 1010
MonotonicityNot monotonic
2021-10-20T18:53:09.794676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
601791
 
0.2%
701289
 
0.1%
801129
 
0.1%
501006
 
0.1%
90991
 
0.1%
65980
 
0.1%
100935
 
0.1%
120806
 
0.1%
75772
 
0.1%
40741
 
0.1%
Other values (1503)49469
 
4.9%
(Missing)940091
94.0%
ValueCountFrequency (%)
170
< 0.1%
229
< 0.1%
326
 
< 0.1%
416
 
< 0.1%
535
< 0.1%
641
< 0.1%
724
 
< 0.1%
832
< 0.1%
926
 
< 0.1%
1068
< 0.1%
ValueCountFrequency (%)
315201001
< 0.1%
173200001
< 0.1%
83800001
< 0.1%
34800001
< 0.1%
20100001
< 0.1%
11500001
< 0.1%
8116001
< 0.1%
7804561
< 0.1%
7715121
< 0.1%
6600001
< 0.1%

price
Real number (ℝ≥0)

HIGH CORRELATION
SKEWED

Distinct20110
Distinct (%)2.0%
Missing509
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean406952885.5
Minimum0
Maximum1.6 × 1012
Zeros62
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.6 MiB
2021-10-20T18:53:09.972638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile750000
Q11800000
median150000000
Q3395000000
95-th percentile1450000000
Maximum1.6 × 1012
Range1.6 × 1012
Interquartile range (IQR)393200000

Descriptive statistics

Standard deviation2421718563
Coefficient of variation (CV)5.950857334
Kurtosis193291.8956
Mean406952885.5
Median Absolute Deviation (MAD)148749000
Skewness315.3462535
Sum4.067457465 × 1014
Variance5.8647208 × 1018
MonotonicityNot monotonic
2021-10-20T18:53:10.127675image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120000015896
 
1.6%
150000013841
 
1.4%
100000012670
 
1.3%
130000012636
 
1.3%
110000011429
 
1.1%
200000011277
 
1.1%
25000000011059
 
1.1%
80000010896
 
1.1%
90000010664
 
1.1%
140000010362
 
1.0%
Other values (20100)878761
87.9%
ValueCountFrequency (%)
062
< 0.1%
901
 
< 0.1%
1502
 
< 0.1%
9501
 
< 0.1%
10001
 
< 0.1%
11001
 
< 0.1%
15001
 
< 0.1%
53451
 
< 0.1%
260001
 
< 0.1%
360002
 
< 0.1%
ValueCountFrequency (%)
1.6 × 10121
 
< 0.1%
4.65 × 10111
 
< 0.1%
3.45 × 10111
 
< 0.1%
3.1 × 10111
 
< 0.1%
2.52 × 10111
 
< 0.1%
1.9 × 10111
 
< 0.1%
1.86 × 10111
 
< 0.1%
1.8375 × 10111
 
< 0.1%
1.83 × 10111
 
< 0.1%
1.8 × 10113
< 0.1%

currency
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing550
Missing (%)0.1%
Memory size7.6 MiB
COP
999402 
USD
 
43
ARS
 
5

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCOP
2nd rowCOP
3rd rowCOP
4th rowCOP
5th rowCOP

Common Values

ValueCountFrequency (%)
COP999402
99.9%
USD43
 
< 0.1%
ARS5
 
< 0.1%
(Missing)550
 
0.1%

Length

2021-10-20T18:53:10.268674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-20T18:53:10.337641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
cop999402
> 99.9%
usd43
 
< 0.1%
ars5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

price_period
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing878833
Missing (%)87.9%
Memory size7.6 MiB
Mensual
121167 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMensual
2nd rowMensual
3rd rowMensual
4th rowMensual
5th rowMensual

Common Values

ValueCountFrequency (%)
Mensual121167
 
12.1%
(Missing)878833
87.9%

Length

2021-10-20T18:53:10.414682image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-20T18:53:10.480676image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
mensual121167
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

title
Categorical

HIGH CARDINALITY

Distinct364029
Distinct (%)36.4%
Missing12
Missing (%)< 0.1%
Memory size7.6 MiB
Apartamento en Arriendo Ubicado en MEDELLIN
 
35411
Apartamento en Venta Ubicado en MEDELLIN
 
29455
Apartamento en Arriendo Ubicado en SABANETA
 
12247
APARTAMENTO EN VENTA EN Bogota
 
11177
Apartamento en Venta Ubicado en SABANETA
 
10733
Other values (364024)
900965 

Length

Max length146
Median length44
Mean length46.10365324
Min length1

Characters and Unicode

Total characters18
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique222959 ?
Unique (%)22.3%

Sample

1st rowCasa Campestre en venta en darien 3469064
2nd rowCasa en ciudsd jardin
3rd rowCasa en ciudsd jardin
4th rowCasa en venta en pance 1630426
5th rowCASA EXTERNA BARRIO CIUDAD 2000

Common Values

ValueCountFrequency (%)
Apartamento en Arriendo Ubicado en MEDELLIN35411
 
3.5%
Apartamento en Venta Ubicado en MEDELLIN29455
 
2.9%
Apartamento en Arriendo Ubicado en SABANETA12247
 
1.2%
APARTAMENTO EN VENTA EN Bogota11177
 
1.1%
Apartamento en Venta Ubicado en SABANETA10733
 
1.1%
Apartamento en Arriendo Ubicado en ENVIGADO9924
 
1.0%
Local en Arriendo Ubicado en MEDELLIN6579
 
0.7%
Casa en Venta Ubicado en MEDELLIN6064
 
0.6%
APARTAMENTO EN ARRIENDO EN Bogota5968
 
0.6%
Oficina en Arriendo Ubicado en MEDELLIN5355
 
0.5%
Other values (364019)867075
86.7%

Length

2021-10-20T18:53:10.613640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
en1309125
 
19.1%
apartamento503261
 
7.3%
venta411841
 
6.0%
arriendo331174
 
4.8%
casa203403
 
3.0%
ubicado176606
 
2.6%
156557
 
2.3%
de135219
 
2.0%
medellin123909
 
1.8%
la88915
 
1.3%
Other values (203839)3420226
49.9%

Most occurring characters

ValueCountFrequency (%)
18
100.0%

Most occurring categories

ValueCountFrequency (%)
Control18
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common18
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
18
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII18
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18
100.0%

description
Categorical

HIGH CARDINALITY

Distinct512446
Distinct (%)51.3%
Missing644
Missing (%)0.1%
Memory size7.6 MiB
En Rentahouse Finca Raiz contamos con más de 51 años de experiencia en la comercialización inmobiliaria. Disponemos de una amplia oferta en Cundinamarca y Antioquia. Contactanos y recibe información del inmueble que sueñas... ¿Te interesa conocer más de nuestro equipo? Siguenos en Instagram Rentahousefincariaz.co o visita nuestra sitio web www.Rentahousefincaraiz.com.co
 
847
Codigo Inmueble 6182 COD INTERNO 6182No pierdas esta oportunidad! Hermoso local en un lugar estratégico amplio con excelente iluminación.
 
435
Codigo Inmueble 574 Local muy amplio con espacios abiertos cercano Av oriental Tiene gas por red mas de 30 oficinas y 30 baños. Disponible para Renta o venta.
 
425
Codigo Inmueble 514 APARTAMENTO PARA ARRIENDO Y VENTA EN UNIDAD CERRADA PARA ESTRENAR, CON MUY BUENOS ACABADOS Y BUENA ILUMINACIÓN.
 
419
Codigo Inmueble 239 Se arrienda oficina en Mayorca con amplio, buena iluminación.Las comodidades son: Salón amplio, piso en madera laminada, aire acondicionado,terraza (área de 10.25 mtrs), 1 baño con lavamanos.(Para Oficinas prestadoras de servicio).
 
408
Other values (512441)
996822 

Length

Max length24398
Median length354
Mean length397.9051319
Min length1

Characters and Unicode

Total characters1874448
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique360528 ?
Unique (%)36.1%

Sample

1st rowHERMOSA CASA CAMPESTRE, &Aacute;REA 6,000 MT, UBICADA EN LA VIA BUGA - BUENAVENTURA, EN PARCELACI&Oacute;N CON UNA EXCELENTE VISTA DEL LAGO CALIMA, SITIO DE ALTA VALORIZACI&Oacute;N, ZONA SUPREMAMENTE APACIBLE, DONDE PODR&Aacute;S DISFRUTAR DE LOS MAS HERMOSOS PAISAJES, Y PODR&Aacute;S APRECIAR LA FAUNA Y LA FLORA DE LA REGI&Oacute;N. A 20 MINUTOS DEL SE&Ntilde;OR DE LOS MILAGROS DE BUGA.<br /><br /> <br /> Ref#571412.
2nd rowCasa independiente con posiciona en ciudad jardín y hermosos jardines. La casa s eve de amoblada Sur se la Ciudad 9 garajes
3rd rowCasa independiente con posiciona en ciudad jardín y hermosos jardines. Amo la casa al Sur se la Ciudad 9 garajes
4th rowEXCELENTE CASA - LOTE 6,373 MT, EN OBRA GRIS UBICADA AL SUR DE LA CIUDAD DE CALI, CONSTRUCCION TIPO MANSION, ADEMAS SE PUEDE CONSIDERAR SU CONSTRUCCION PARA UN HOTEL, SPA, CENTRO DE ESTETICA, CLINICA DE REPOSO, ETC, ZONA SUPER APACIBLE, CINCO SUPER HABITACIONES CADA UNA CON BA&Ntilde;O, VESTIER, BALCON, ASCENSOR, ALCOBA PRINCIPAL CON DOBLE VESTIER, DOBLE BA&Ntilde;O, PISCINA, ZONA HUMEDA, ZONAS VERDES, PARQUEADEROS EN SOTANO. SE VENDE TAMBIEN COMO LOTE.<br /><br /> <br /> Ref#571457.
5th rowCASA EXTERNA EN EL BARRIO CIUDAD 2000,CONSTRUIDA EN TRES PISOS , CON CUATRO APARTA ESTUDIOS CON SERVICIOS INDEPENDIENTES , RECIBE RENTA MENSUAL , PLANCHA Y CIMIENTOS CON SOLUCI&Oacute;N DE SERVICIOS PARA CONSTRUIR CUARTO PISO , GARAJE ANTEJARDIN , CERCA AL NUEVO PUENTE DE LA AVENIDA CIUDAD DE CALI<br /><br /> <br /> Ref#571369.

Common Values

ValueCountFrequency (%)
En Rentahouse Finca Raiz contamos con más de 51 años de experiencia en la comercialización inmobiliaria. Disponemos de una amplia oferta en Cundinamarca y Antioquia. Contactanos y recibe información del inmueble que sueñas... ¿Te interesa conocer más de nuestro equipo? Siguenos en Instagram Rentahousefincariaz.co o visita nuestra sitio web www.Rentahousefincaraiz.com.co847
 
0.1%
Codigo Inmueble 6182 COD INTERNO 6182No pierdas esta oportunidad! Hermoso local en un lugar estratégico amplio con excelente iluminación.435
 
< 0.1%
Codigo Inmueble 574 Local muy amplio con espacios abiertos cercano Av oriental Tiene gas por red mas de 30 oficinas y 30 baños. Disponible para Renta o venta.425
 
< 0.1%
Codigo Inmueble 514 APARTAMENTO PARA ARRIENDO Y VENTA EN UNIDAD CERRADA PARA ESTRENAR, CON MUY BUENOS ACABADOS Y BUENA ILUMINACIÓN.419
 
< 0.1%
Codigo Inmueble 239 Se arrienda oficina en Mayorca con amplio, buena iluminación.Las comodidades son: Salón amplio, piso en madera laminada, aire acondicionado,terraza (área de 10.25 mtrs), 1 baño con lavamanos.(Para Oficinas prestadoras de servicio).408
 
< 0.1%
Codigo Inmueble 561 Casa cerca al Éxito Laureles amplios espacios con muy buenas rutas de trabajo398
 
< 0.1%
Codigo Inmueble 667 Se vende o arrenda oficina con un área de 37 mts, una cocineta, un baño, salón comedor, balcón, ascensor, porqueadero.342
 
< 0.1%
Codigo Inmueble 6421 Hermosa casa con 3 habitaciones, closets, sala-comedor, cocina integral con red de gas, 3 baños completos. Iluminado, buena ventilación, cómodas áreas hacen de este inmueble un lugar único para vivir, cerca de supermercados, restaurantes. Porque los asuntos importantes deben quedar en manos de expertos, llámanos para ampliarte la información.311
 
< 0.1%
Codigo Inmueble 6333 No pierdas esta oportunidad!! Hermoso apartamento con 3 habitaciones, 1 baño completo, 1 baño social, garaje sencillo. Iluminado, buena ventilación, cómodas áreas hacen de este inmueble un lugar único para vivir, cerca de Centros Comerciales, supermercados, restaurantes. Porque los asuntos importantes deben quedar en manos de expertos, llámanos para ampliarte la información. Código Interno: 6333309
 
< 0.1%
Codigo Inmueble 6047 No pierdas esta oportunidad!! Hermosa oficina con 5 puestos de trabajo completos,1 baño, 1 cocineta, en edificio con portería, parqueadero cubierto de visitantes, Se renta con muebles o sin muebles. Porque los asuntos importantes deben quedar en manos de expertos, llámanos para ampliarte la información. Código Interno: 6047303
 
< 0.1%
Other values (512436)995159
99.5%
(Missing)644
 
0.1%

Length

2021-10-20T18:53:10.831639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
de3561573
 
5.9%
con2081924
 
3.4%
en1826784
 
3.0%
y1802074
 
3.0%
1111327
 
1.8%
la1061780
 
1.8%
el756196
 
1.3%
a674786
 
1.1%
para666916
 
1.1%
zona623508
 
1.0%
Other values (427660)46203880
76.5%

Most occurring characters

ValueCountFrequency (%)
1874448
100.0%

Most occurring categories

ValueCountFrequency (%)
Control1874448
100.0%

Most frequent character per category

Control
ValueCountFrequency (%)
1874448
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1874448
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1874448
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII1874448
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1874448
100.0%

property_type
Categorical

HIGH CORRELATION

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
Apartamento
565404 
Casa
220101 
Otro
111046 
Lote
 
46369
Local comercial
 
26389
Other values (4)
 
30691

Length

Max length15
Median length11
Mean length8.32893
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCasa
2nd rowCasa
3rd rowCasa
4th rowCasa
5th rowCasa

Common Values

ValueCountFrequency (%)
Apartamento565404
56.5%
Casa220101
 
22.0%
Otro111046
 
11.1%
Lote46369
 
4.6%
Local comercial26389
 
2.6%
Oficina22258
 
2.2%
Finca6725
 
0.7%
Depósito1544
 
0.2%
Parqueadero164
 
< 0.1%

Length

2021-10-20T18:53:10.974642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-20T18:53:11.057642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
apartamento565404
55.1%
casa220101
 
21.4%
otro111046
 
10.8%
lote46369
 
4.5%
local26389
 
2.6%
comercial26389
 
2.6%
oficina22258
 
2.2%
finca6725
 
0.7%
depósito1544
 
0.2%
parqueadero164
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

operation_type
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.6 MiB
Venta
571884 
Arriendo
427736 
Arriendo temporal
 
380

Length

Max length17
Median length5
Mean length6.287768
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVenta
2nd rowVenta
3rd rowVenta
4th rowVenta
5th rowVenta

Common Values

ValueCountFrequency (%)
Venta571884
57.2%
Arriendo427736
42.8%
Arriendo temporal380
 
< 0.1%

Length

2021-10-20T18:53:11.179641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-20T18:53:11.250680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
venta571884
57.2%
arriendo428116
42.8%
temporal380
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-10-20T18:52:52.985630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:40.693629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:42.939628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:44.888628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:46.363664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:48.136632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:50.201665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:51.517666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:53.431628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:41.113632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:43.292629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:45.082628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:46.608629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:48.644664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:50.357679image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:51.664664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:53.615629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:41.313629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:43.482629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:45.268630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:46.792629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:48.798663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:50.521629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:51.836666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:53.848629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:41.578663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:43.743628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:45.463631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:47.034628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:49.022629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:50.686629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:51.995665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:54.199631image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:41.906629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:44.025632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:45.628663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:47.240628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:49.360629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:50.846632image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:52.147629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:54.360633image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:42.082664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:44.180629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:45.783666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:47.397666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:49.518664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:51.008665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:52.307666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:54.528629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:42.243670image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:44.356669image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:45.948630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:47.560629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:49.687664image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:51.177667image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:52.469630image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:54.928629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:42.578628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:44.711629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:46.139628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:47.809629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:50.036628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:51.344666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-10-20T18:52:52.640629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-10-20T18:53:11.327639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-20T18:53:11.496639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-20T18:53:11.663639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-20T18:53:11.833639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-20T18:53:12.117674image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-20T18:52:55.892629image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-20T18:52:58.008628image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-10-20T18:53:02.803638image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-10-20T18:53:03.993639image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

idad_typestart_dateend_datecreated_onlatlonl1l2l3l4l5l6roomsbedroomsbathroomssurface_totalsurface_coveredpricecurrencyprice_periodtitledescriptionproperty_typeoperation_type
0KsjahK62rxcYKXXQjOdkqw==Propiedad2020-10-072021-10-092020-10-073.9210-76.506000ColombiaValle del CaucaNaNNaNNaNNaNNaN6.07.0NaNNaN1.300000e+09COPNaNCasa Campestre en venta en darien 3469064HERMOSA CASA CAMPESTRE, &Aacute;REA 6,000 MT, UBICADA EN LA VIA BUGA - BUENAVENTURA, EN PARCELACI&Oacute;N CON UNA EXCELENTE VISTA DEL LAGO CALIMA, SITIO DE ALTA VALORIZACI&Oacute;N, ZONA SUPREMAMENTE APACIBLE, DONDE PODR&Aacute;S DISFRUTAR DE LOS MAS HERMOSOS PAISAJES, Y PODR&Aacute;S APRECIAR LA FAUNA Y LA FLORA DE LA REGI&Oacute;N. A 20 MINUTOS DEL SE&Ntilde;OR DE LOS MILAGROS DE BUGA.<br /><br />\n <br />\n Ref#571412.CasaVenta
1Y+gsBZYq1zu5NoR3V5oUGA==Propiedad2020-10-072021-01-062020-10-073.3577-76.541811ColombiaValle del CaucaCaliCiudad JardínNaNNaNNaNNaN7.0NaNNaN2.800000e+09COPNaNCasa en ciudsd jardinCasa independiente con posiciona en ciudad jardín y hermosos jardines. La casa s eve de amoblada\nSur se la\nCiudad 9 garajesCasaVenta
2Jpzqxj8/Vgf3Aa5ASxUBNg==Propiedad2020-10-072020-10-072020-10-073.3577-76.541811ColombiaValle del CaucaCaliCiudad JardínNaNNaNNaNNaN7.0NaNNaN2.800000e+09COPMensualCasa en ciudsd jardinCasa independiente con posiciona en ciudad jardín y hermosos jardines. Amo la casa al\nSur se la\nCiudad 9 garajesCasaVenta
3ieuFnkFx/yHDD66iMV14Gw==Propiedad2020-10-072021-04-122020-10-073.3640-76.538000ColombiaValle del CaucaCaliCiudad JardínNaNNaNNaN5.08.0NaNNaN3.500000e+09COPNaNCasa en venta en pance 1630426EXCELENTE CASA - LOTE 6,373 MT, EN OBRA GRIS UBICADA AL SUR DE LA CIUDAD DE CALI, CONSTRUCCION TIPO MANSION, ADEMAS SE PUEDE CONSIDERAR SU CONSTRUCCION PARA UN HOTEL, SPA, CENTRO DE ESTETICA, CLINICA DE REPOSO, ETC, ZONA SUPER APACIBLE, CINCO SUPER HABITACIONES CADA UNA CON BA&Ntilde;O, VESTIER, BALCON, ASCENSOR, ALCOBA PRINCIPAL CON DOBLE VESTIER, DOBLE BA&Ntilde;O, PISCINA, ZONA HUMEDA, ZONAS VERDES, PARQUEADEROS EN SOTANO. SE VENDE TAMBIEN COMO LOTE.<br /><br />\n <br />\n Ref#571457.CasaVenta
4g4u5JM+hAHEk8SukRSjMzg==Propiedad2020-10-079999-12-312020-10-073.3910-76.517000ColombiaValle del CaucaCaliNaNNaNNaNNaN8.09.0NaNNaN4.800000e+08COPNaNCASA EXTERNA BARRIO CIUDAD 2000CASA EXTERNA EN EL BARRIO CIUDAD 2000,CONSTRUIDA EN TRES PISOS , CON CUATRO APARTA ESTUDIOS CON SERVICIOS INDEPENDIENTES , RECIBE RENTA MENSUAL , PLANCHA Y CIMIENTOS CON SOLUCI&Oacute;N DE SERVICIOS PARA CONSTRUIR CUARTO PISO , GARAJE ANTEJARDIN , CERCA AL NUEVO PUENTE DE LA AVENIDA CIUDAD DE CALI<br /><br />\n <br />\n Ref#571369.CasaVenta
5+S9T8NKJ/yyndMxxRz1emQ==Propiedad2020-10-079999-12-312020-10-073.4130-76.544000ColombiaValle del CaucaCaliNaNNaNNaNNaN10.010.0NaNNaN1.400000e+09COPNaNCASA PARA VENTA NUEVA TEQUENDAMACASA EXTERNA CONSTRUIDA EN TRES PISOS , EN EL BARRIO NUEVA TEQUENDAMA IDEAL PARA CLINICA, LABORATORIO CL&Iacute;NICO, CENTRO DE EST&Eacute;TICA , HOTEL ,APARTAESTUDIOS . CERCA A LA CARRERA 42 Y 44A , F&Aacute;CIL ACCESO A TRANSPORTE PUBLICO , SU UBICACI&Oacute;N ES ESTRAT&Eacute;GICA POR ESTAR RODEADA DE LAS DIFERENTES EPS Y IPS DE LA CIUDAD<br /><br />\n <br />\n Ref#571206.CasaVenta
60DBic9QJv2FL9Oq0S+xaSA==Propiedad2020-10-072020-10-222020-10-073.4510-76.532000ColombiaValle del CaucaCaliNaNNaNNaNNaN7.07.0NaNNaN2.800000e+08COPNaNCasa en venta en zona norte 3558799CASA EN PASAJE NO V&Iacute;A VEHICULAR UBICADA A DOS CASAS EN EL BARRIO LARES DE COMFENALCO, ESTRATO DOS , CONSTRUIDA EN CUATRO PISOS AS&Iacute;: PRIMER PISO DOS ALCOBAS DOS BA&Ntilde;OS, COCINA INTEGRAL Y ZONA DE OFICIOS , SEGUNDO Y TERCER PISO DOS ALCOBAS , DOS BA&Ntilde;OS , COCINA INTEGRAL , BALCON , CUARTO PISO ZONA DE LAVANDER&Iacute;A Y ESTADERO PARA LOS PISOS 2 Y 3 MAS ALCOBA CON BA&Ntilde;O INDEPENDIENTE<br /><br />\n <br />\n Ref#571462.CasaVenta
70/S4PBWArkYd3NDMyZZtPQ==Propiedad2020-10-072021-06-222020-10-073.4270-76.542000ColombiaValle del CaucaCaliSan Fernando NuevoNaNNaNNaN8.07.0NaNNaN5.300000e+08COPNaNCasa en venta en san fernando 3476093VENDE CASA EN EL SECTOR DE SAN FERNANDO VIEJO, LA CASA ESTA DIVIDIDA EN CUATRO APARTAESTUDIOS DE DOS HABITACIONES, UN BA&Ntilde;O Y COCINETA CADA UNO, EXCELENTE UBICACI&Oacute;N, VARIAS V&Iacute;AS DE ACCESO, SUPERMERCADOS Y CENTROS COMERCIALES CERCANOS.<br /><br />\n <br />\n Ref#571455.CasaVenta
8mrYI162HS5qJaAFN2WpP2Q==Propiedad2020-10-072021-02-112020-10-073.3640-76.538000ColombiaValle del CaucaCaliCiudad JardínNaNNaNNaN5.09.0NaNNaN7.000000e+06COPNaNALQUILA EXCELENTE CASA EN CIUDAD JARDINEXCELENTE VIVIENDA UBICADA SOBRE VIA PRINCIPAL EN EL SECTOR DE CIUDAD JARD&Iacute;N, AMPLIA, CUENTA CON CINCO HABITACIONES, TODAS CON BA&Ntilde;O, SALA, COMEDOR, COCINA INTEGRAL, ZONAS VERDES, PISCINA PROPIA, PARQUEADERO INTERIOR PARA CUATRO VEH&Iacute;CULOS, SE ALQUILA PARA VIVIENDA U OFICINAS, TRANSPORTE PUBLICO CERCANO..<br /><br />\n <br />\n Ref#571281.CasaArriendo
93q8MmfVZHtWJa6pxNnypeg==Propiedad2020-10-079999-12-312020-10-073.5650-76.551000ColombiaValle del CaucaCaliNaNNaNNaNNaN5.07.0NaNNaN1.700000e+09COPNaNHERMOSA CASA EN VENTA EN JAMUNDÍ 2844215EXCELENTE CASA EN CONDOMINIO EXCLUSIVO, &Aacute;REA LOTE 4.000 METROS, &Aacute;REA CONSTRUIDA 926 METROS, ESTILO AMERICANO, LA MEJOR UBICACI&Oacute;N CUENTA CON UNE HERMOSA VISTA AL LAGO, BUENAS V&Iacute;AS DE ACCESO, UNIPLANTA, FAMILY ROOM, 2 CASAS EN EL MISMO PREDIO, ALJIBE, PLANTA EL&Eacute;CTRICA, HIDROFLOW, TANQUES DE RESERVA. CASA ADICIONAL CON 4 ALCOBAS, 4 BA&Ntilde;OS, SALA COMEDOR, COCINA ABIERTA. PISOS CER&Aacute;MICA ESPA&Ntilde;OLA, BA&Ntilde;OS CORONA, COCINA ABIERTA, ESTILO AMERICANO. EL CONDOMINIO CUENTA CON PISCINAS CANCHAS DE F&Uacute;TBOL, TENIS,BASKETBOL, LAGO.<br /><br />\n <br />\n Ref#571391.CasaVenta

Last rows

idad_typestart_dateend_datecreated_onlatlonl1l2l3l4l5l6roomsbedroomsbathroomssurface_totalsurface_coveredpricecurrencyprice_periodtitledescriptionproperty_typeoperation_type
999990k8BXoVsgP8ACJ19UGGGWkQ==Propiedad2021-03-312021-05-102021-03-314.643000-74.135000ColombiaCundinamarcaBogotá D.CZona SuroccidentalKennedyNaNNaN4.02.0NaNNaN1600000.0COPNaNCASA EN ARRIENDO EN BogotaArriendo casa de 3 niveles en Villa Alsacia con 4 alcobas con closet, 2 baños, sala comedor,cocina semint a gas, calentador eléctrico, patio con lavadero,cbs, pisos del comedor en cerámica,pisos sala y alcobas en alfombra,inst.lavadora,garaje cubierto.CasaArriendo
9999915zYdgqEmM+HSCrpPw/2vrw==Propiedad2021-03-312021-05-122021-03-314.644055-74.128511ColombiaCundinamarcaBogotá D.CZona SuroccidentalKennedyNaN3.03.0NaNNaNNaN410000000.0COPNaNAPARTAMENTO EN VENTA VILLA ALSACIAHermoso apartamento en venta, cuenta con una excelente ubicación, cerca de centros comerciales, zonas verdes, parques públicos, avenidas principales, transporte público de fácil acceso. Cuenta con un gran espacio para sala-comedor, 3 habitaciones closet con sus respectivos armarios empotrados, 2 baños, estudio, cocina tipo americana abierta con barra auxiliar, la división de los baños es en vidrio templado, piso en madera laminada y baldosa, piso 11, vista exterior, remodelado, el sector es muy tranquilo, estrato 4, parqueadero propio cubierto, parqueadero para visitantes, parque infantil, gimnasio, zona BBQ, salones sociales. \n\n¡El equipo de Inversiones Inmobiliarias Global Home está comprometida a brindar una experiencia de vida excepcional para nuestros clientes desde el momento en que se unen a nuestra comunidad!\n¡Pide tu cita!\nApartamentoVenta
999992id160MFAX1qSWF79oy010Q==Propiedad2021-03-319999-12-312021-03-314.653064-74.161088ColombiaCundinamarcaBogotá D.CZona SuroccidentalKennedyNaNNaNNaN3.0NaNNaN340000000.0COPNaNCasa conjunto Cabo Verde ( ricaurte - cundinarmarca )Hermosa casa en bello conjunto cerrado , 2 años de construcción y 1 año de tenerla pero no se vive alla solo de casa de vereneoCasaVenta
9999932dTHgLrpas4IYRHxffvu5Q==Propiedad2021-03-319999-12-312021-03-314.640417-74.155487ColombiaCundinamarcaBogotá D.CZona SuroccidentalKennedyNaNNaN2.01.0NaNNaN800000.0COPNaNTORRES DE CASTELLO<b>TORRES DE CASTELLO</b><br><br>APARTAMENTO SALA COMEDOR, 2 ALCOBAS, 1 BA&Ntilde;O, COCINA INTEGRAL, ZONA DE LAVANDER&Iacute;A, CALENTADOR, CLOSETS, ASCENSOR.<br />\nCERCA A, BIBLIOTECA TINTAL, CENTRO COMERCIAL TINTAL, BANDERAS,<br />\nVIAS DE ACCESO, AV CALI, AV AMERICAS, AV BOYACA.<br /><br><br> Características adicionales: <br> - Agua corriente<br> <br><br> Ref#722521.ApartamentoArriendo
999994WuCAZxlgtkYgoHRAiodCbA==Propiedad2021-03-312021-09-032021-03-314.641292-74.153875ColombiaCundinamarcaBogotá D.CZona SuroccidentalKennedyNaNNaNNaN2.0NaNNaN350000000.0COPNaNApartamenton venta Zapan de castilla _ wasi3625614Apartamento en piso 6 con amplia iluminación natural. 81 M2 distribuidos en; Sala comedor, balcón, chimenea funcional a gas, 3 habitaciones cada una con closet, principal con vestier y baño privado, estudio, sala de Star , cocina integral a gas, zona de lavandería. Pisos totalmente laminados en cada espacio. Garaje cubierto. Apartamento ubicado a pocos minutos de la Av ciudad de cali costado oriental y a pocos minutos de la estación de Transmilenio Banderas.ApartamentoVenta
999995HogfsSTtBvNDJkf98/FGIw==Propiedad2021-03-312021-05-182021-03-316.186412-75.658630ColombiaAntioquiaMedellínSan Antonio de PradoNaNNaNNaN2.01.0NaNNaN700000.0COPNaNAPARTAMENTO EN ARRIENDO, SAN ANTONIO DE PRADO-PRADITOApartamento de 48 Mts2 ubicado en conjunto cerrado con excelentes zonas de esparcimiento y buenas rutas de acceso vial, espacios amplios e iluminados. Ubicado en excelente sector, cerca al parque industrial de pradoApartamentoArriendo
9999961LxE1UMbfMeW5Dv/z4rqJA==Propiedad2021-03-312021-05-182021-03-316.186412-75.658630ColombiaAntioquiaMedellínSan Antonio de PradoNaNNaNNaN2.01.0NaNNaN700000.0COPNaNAPARTAMENTO EN ARRIENDO, SAN ANTONIO DE PRADO-PRADITOApartamento de 48 Mts2 ubicado en conjunto cerrado con excelentes zonas de esparcimiento y buenas rutas de acceso vial, espacios amplios e iluminados. Ubicado en excelente sector, cerca al parque industrial de pradoApartamentoArriendo
999997NEIrzJXLpHqPDIXwD+9r8w==Propiedad2021-03-312021-05-072021-03-316.186412-75.658630ColombiaAntioquiaMedellínSan Antonio de PradoNaNNaNNaN2.01.0NaNNaN700000.0COPNaNAPARTAMENTO EN ARRIENDO, SAN ANTONIO DE PRADO-PRADITOApartamento de 48 Mts2 ubicado en conjunto cerrado con excelentes zonas de esparcimiento y buenas rutas de acceso vial, espacios amplios e iluminados. Ubicado en excelente sector, cerca al Parque Industrial de Prado. \n\nApartamentoArriendo
999998fpxqXlGPqDwKhuZVQGxCPA==Propiedad2021-03-319999-12-312021-03-3111.015336-74.831347ColombiaAtlánticoBarranquillaPaseo de la CastellanaNaNNaNNaN3.04.0NaNNaN700000000.0COPNaNCasa en venta La Castellana<b>Casa en venta La Castellana</b><br><br>Hermosa casa de 2 niveles, cocina tipo americano, 3 habitaciones cada una con ba&ntilde;o, mas ba&ntilde;o social, cuarto y ba&ntilde;o de servicio, &aacute;rea de labores, patio, estudio cerrado con drybol que puede ser una cuarta habitaci&oacute;n, balc&oacute;n, 2 parqueaderos, conjunto con piscina, parque, gym, cancha m&uacute;ltiple, cerca a pricesmart, adm$387.000.<br /><br><br> Características adicionales: <br> <br><br> Ref#722557.CasaVenta
999999ZU6bNwudfah9N7ma06509Q==Propiedad2021-03-319999-12-312021-03-3111.017029-74.834101ColombiaAtlánticoBarranquillaPaseo de la CastellanaNaNNaN3.03.0NaNNaNNaN645000000.0COPNaNVENTA DE APARTAMENTO EN ZONA EXCLUSIVA DEL NORTE DE BARRANQUILLAHERMOSO APARTAMENTO UBICADO EN ZONA EXCLUSIVA DEL NORTE DE BARRANQUILLA CUENTA CON EXCELENTE Y AMPLIAS AREAS SOCIALES, PISCINA, GIMNASIO, SALON SOCIAL, PARQUE INFANTIL, CADA HABITACION TIENE SU BAÑO, AMPLIO, CON ILUMINACION NATURAL Y GRAN VENTILACION, SE ENTREGA CON AIRES EN LA SALA, CALENTADOR Y CORTINAS BLACKOUTS.\nApartamentoVenta